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Roundup 10/28/2016

I’m publishing a three-part series on the state of enterprise machine learning in The Next Platform. Part one is here.

Good Reads

— Helen Beers explains AI in the second part of a series. Part one is here.

— Market Realist publishes a twelve-part series on the outlook for NVIDIA. Don’t worry, they’re short parts.

Issues

— In Wired, Clive Thompson whines that AI isn’t accountable, citing an example where a consumer’s application for insurance is declined without explanation. It’s pretty clear that Clive Thompson has no idea what he’s writing about:

AI-driven applications are accountable to the people who build them. Banks and insurance companies don’t want to decline your business; they want to approve your application unless you’re a bad risk.

A biased model is bad for business, and the data scientists who build them know it. That’s why they constantly monitor models for systematic errors.

Any predictive model, including “black box” learners, can be designed to deliver explanations for rejects. In regulated industries like banking and insurance, this is legally required.

Other than that, it’s a fine article.

Methods and Techniques

— Researchers at Google Brain publish a paper on how neural networks can learn to use secret keys to protect information when they communicate with one another.

— Daniel Tunkelang, formerly of LinkedIn and Google, is skeptical that we can automate ML.

— If you’re inclined to feel that convolutional neural networks get way too much buzz and recurrent neural networks are underappreciated, William Vorhies is on your side.

Competitions

— Kaggle launches a product recommendation challenge for Santander Bank. Submissions due 11:59pm Wednesday, 21 December UTC. First place gets $30K; second place gets $20K; third place gets $10K; fourth place gets a set of steak knives.

Applications

— Twitter says it will use ML to make your timeline more relevant. No word on whether they plan to use ML to decide who to fire.

ML/DL Software and Services

— Databricks adds support for DL with TensorFrames, a Spark package that enables TensorFlow to run on Spark. The managed service includes support for TensorFrames with GPU-accelerated compute instances. Alex Woodie reports, noting that Spark ML runs 10X faster on GPUs.

Hardware and Provisioning

— Scientific Computing World reports on the challenges of managing an HPC cluster for applications like deep learning.

— Rich Brueckner asks if deep learning will scale to supercomputers. The answer seems to be “yes”.